Overview

Dataset statistics

Number of variables9
Number of observations768
Missing cells77
Missing cells (%)1.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory54.1 KiB
Average record size in memory72.2 B

Variable types

NUM8
BOOL1

Warnings

BloodPressure has 33 (4.3%) missing values Missing
BMI has 16 (2.1%) missing values Missing
Age has 22 (2.9%) missing values Missing
Pregnancies has 111 (14.5%) zeros Zeros
BloodPressure has 34 (4.4%) zeros Zeros
SkinThickness has 227 (29.6%) zeros Zeros
Insulin has 372 (48.4%) zeros Zeros
BMI has 10 (1.3%) zeros Zeros

Reproduction

Analysis started2021-05-12 21:51:05.258954
Analysis finished2021-05-12 21:51:15.392791
Duration10.13 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Pregnancies
Real number (ℝ≥0)

ZEROS

Distinct17
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.845052083
Minimum0
Maximum17
Zeros111
Zeros (%)14.5%
Memory size6.0 KiB
2021-05-12T14:51:15.448494image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10
Maximum17
Range17
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.369578063
Coefficient of variation (CV)0.8763413316
Kurtosis0.1592197775
Mean3.845052083
Median Absolute Deviation (MAD)2
Skewness0.9016739792
Sum2953
Variance11.35405632
MonotocityNot monotonic
2021-05-12T14:51:15.524430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%) 
113517.6%
 
011114.5%
 
210313.4%
 
3759.8%
 
4688.9%
 
5577.4%
 
6506.5%
 
7455.9%
 
8384.9%
 
9283.6%
 
Other values (7)587.6%
 
ValueCountFrequency (%) 
011114.5%
 
113517.6%
 
210313.4%
 
3759.8%
 
4688.9%
 
ValueCountFrequency (%) 
1710.1%
 
1510.1%
 
1420.3%
 
13101.3%
 
1291.2%
 

Glucose
Real number (ℝ≥0)

Distinct136
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.8945312
Minimum0
Maximum199
Zeros5
Zeros (%)0.7%
Memory size6.0 KiB
2021-05-12T14:51:15.626900image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79
Q199
median117
Q3140.25
95-th percentile181
Maximum199
Range199
Interquartile range (IQR)41.25

Descriptive statistics

Standard deviation31.9726182
Coefficient of variation (CV)0.2644670347
Kurtosis0.6407798204
Mean120.8945312
Median Absolute Deviation (MAD)20
Skewness0.1737535018
Sum92847
Variance1022.248314
MonotocityNot monotonic
2021-05-12T14:51:15.726598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
100172.2%
 
99172.2%
 
129141.8%
 
125141.8%
 
111141.8%
 
106141.8%
 
95131.7%
 
108131.7%
 
105131.7%
 
102131.7%
 
Other values (126)62681.5%
 
ValueCountFrequency (%) 
050.7%
 
4410.1%
 
5610.1%
 
5720.3%
 
6110.1%
 
ValueCountFrequency (%) 
19910.1%
 
19810.1%
 
19740.5%
 
19630.4%
 
19520.3%
 

BloodPressure
Real number (ℝ≥0)

MISSING
ZEROS

Distinct47
Distinct (%)6.4%
Missing33
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean68.92789116
Minimum0
Maximum122
Zeros34
Zeros (%)4.4%
Memory size6.0 KiB
2021-05-12T14:51:15.829561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35.6
Q162
median72
Q380
95-th percentile90.6
Maximum122
Range122
Interquartile range (IQR)18

Descriptive statistics

Standard deviation19.43364236
Coefficient of variation (CV)0.2819416354
Kurtosis5.090630321
Mean68.92789116
Median Absolute Deviation (MAD)8
Skewness-1.83501548
Sum50662
Variance377.6664554
MonotocityNot monotonic
2021-05-12T14:51:15.928501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%) 
70557.2%
 
74486.2%
 
68445.7%
 
72425.5%
 
64415.3%
 
78415.3%
 
76395.1%
 
80384.9%
 
60364.7%
 
0344.4%
 
Other values (37)31741.3%
 
ValueCountFrequency (%) 
0344.4%
 
2410.1%
 
3020.3%
 
3810.1%
 
4010.1%
 
ValueCountFrequency (%) 
12210.1%
 
11410.1%
 
11020.3%
 
10820.3%
 
10630.4%
 

SkinThickness
Real number (ℝ≥0)

ZEROS

Distinct51
Distinct (%)6.6%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean20.52151239
Minimum0
Maximum99
Zeros227
Zeros (%)29.6%
Memory size6.0 KiB
2021-05-12T14:51:16.029826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q332
95-th percentile44
Maximum99
Range99
Interquartile range (IQR)32

Descriptive statistics

Standard deviation15.95724522
Coefficient of variation (CV)0.7775862188
Kurtosis-0.5198867202
Mean20.52151239
Median Absolute Deviation (MAD)12
Skewness0.1117441161
Sum15740
Variance254.633675
MonotocityNot monotonic
2021-05-12T14:51:16.126417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
022729.6%
 
32303.9%
 
30273.5%
 
27233.0%
 
23222.9%
 
33202.6%
 
28202.6%
 
18202.6%
 
31192.5%
 
39182.3%
 
Other values (41)34144.4%
 
ValueCountFrequency (%) 
022729.6%
 
720.3%
 
820.3%
 
1050.7%
 
1160.8%
 
ValueCountFrequency (%) 
9910.1%
 
6310.1%
 
6010.1%
 
5610.1%
 
5420.3%
 

Insulin
Real number (ℝ≥0)

ZEROS

Distinct185
Distinct (%)24.2%
Missing5
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean79.95806029
Minimum0
Maximum846
Zeros372
Zeros (%)48.4%
Memory size6.0 KiB
2021-05-12T14:51:16.224600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median29
Q3127.5
95-th percentile293
Maximum846
Range846
Interquartile range (IQR)127.5

Descriptive statistics

Standard deviation115.4638861
Coefficient of variation (CV)1.444055618
Kurtosis7.18933386
Mean79.95806029
Median Absolute Deviation (MAD)29
Skewness2.269856301
Sum61008
Variance13331.909
MonotocityNot monotonic
2021-05-12T14:51:16.316608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
037248.4%
 
105111.4%
 
13091.2%
 
14091.2%
 
12081.0%
 
9470.9%
 
18070.9%
 
10070.9%
 
11560.8%
 
11060.8%
 
Other values (175)32141.8%
 
ValueCountFrequency (%) 
037248.4%
 
1410.1%
 
1510.1%
 
1610.1%
 
1820.3%
 
ValueCountFrequency (%) 
84610.1%
 
74410.1%
 
68010.1%
 
60010.1%
 
57910.1%
 

BMI
Real number (ℝ≥0)

MISSING
ZEROS

Distinct247
Distinct (%)32.8%
Missing16
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean32.00066489
Minimum0
Maximum67.1
Zeros10
Zeros (%)1.3%
Memory size6.0 KiB
2021-05-12T14:51:16.415195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21.8
Q127.3
median32
Q336.525
95-th percentile44.2
Maximum67.1
Range67.1
Interquartile range (IQR)9.225

Descriptive statistics

Standard deviation7.795928268
Coefficient of variation (CV)0.2436176965
Kurtosis3.270969076
Mean32.00066489
Median Absolute Deviation (MAD)4.6
Skewness-0.3907930616
Sum24064.5
Variance60.77649756
MonotocityNot monotonic
2021-05-12T14:51:16.644628image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32131.7%
 
31.2121.6%
 
31.6121.6%
 
0101.3%
 
33.3101.3%
 
32.4101.3%
 
32.991.2%
 
30.191.2%
 
30.891.2%
 
34.281.0%
 
Other values (237)65084.6%
 
(Missing)162.1%
 
ValueCountFrequency (%) 
0101.3%
 
18.230.4%
 
18.410.1%
 
19.110.1%
 
19.310.1%
 
ValueCountFrequency (%) 
67.110.1%
 
59.410.1%
 
57.310.1%
 
5510.1%
 
53.210.1%
 

DiabetesPedigreeFunction
Real number (ℝ≥0)

Distinct517
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4718763021
Minimum0.078
Maximum2.42
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-05-12T14:51:16.749569image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.078
5-th percentile0.14035
Q10.24375
median0.3725
Q30.62625
95-th percentile1.13285
Maximum2.42
Range2.342
Interquartile range (IQR)0.3825

Descriptive statistics

Standard deviation0.331328595
Coefficient of variation (CV)0.7021513764
Kurtosis5.594953528
Mean0.4718763021
Median Absolute Deviation (MAD)0.1675
Skewness1.919911066
Sum362.401
Variance0.1097786379
MonotocityNot monotonic
2021-05-12T14:51:16.852790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.25460.8%
 
0.25860.8%
 
0.25950.7%
 
0.23850.7%
 
0.20750.7%
 
0.26850.7%
 
0.26150.7%
 
0.16740.5%
 
0.1940.5%
 
0.2740.5%
 
Other values (507)71993.6%
 
ValueCountFrequency (%) 
0.07810.1%
 
0.08410.1%
 
0.08520.3%
 
0.08820.3%
 
0.08910.1%
 
ValueCountFrequency (%) 
2.4210.1%
 
2.32910.1%
 
2.28810.1%
 
2.13710.1%
 
1.89310.1%
 

Age
Real number (ℝ≥0)

MISSING

Distinct52
Distinct (%)7.0%
Missing22
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean33.24932976
Minimum21
Maximum81
Zeros0
Zeros (%)0.0%
Memory size6.0 KiB
2021-05-12T14:51:16.951030image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile21
Q124
median29
Q341
95-th percentile58
Maximum81
Range60
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.77698955
Coefficient of variation (CV)0.3542023142
Kurtosis0.6303458196
Mean33.24932976
Median Absolute Deviation (MAD)7
Skewness1.127863594
Sum24804
Variance138.6974828
MonotocityNot monotonic
2021-05-12T14:51:17.045665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
22699.0%
 
21617.9%
 
25486.2%
 
24455.9%
 
23374.8%
 
28344.4%
 
26324.2%
 
27324.2%
 
29283.6%
 
31233.0%
 
Other values (42)33743.9%
 
(Missing)222.9%
 
ValueCountFrequency (%) 
21617.9%
 
22699.0%
 
23374.8%
 
24455.9%
 
25486.2%
 
ValueCountFrequency (%) 
8110.1%
 
7210.1%
 
7010.1%
 
6920.3%
 
6810.1%
 

Outcome
Boolean

Distinct2
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size6.0 KiB
0
500 
1
268 
ValueCountFrequency (%) 
050065.1%
 
126834.9%
 
2021-05-12T14:51:17.115325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Interactions

2021-05-12T14:51:08.382485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:08.497172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:08.606220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:08.711001image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:08.810199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:08.907057image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.008771image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.110798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.212684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.324972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.437992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.544656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.647830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.747669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.851155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:09.955666image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.059859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.161674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.267179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.366808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.547719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.639622image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.737221image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.834256image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:10.931850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.030037image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.130683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.226994image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.320354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.409600image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.503863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.597980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.691996image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.784035image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.879611image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:11.970415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.056931image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.141439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.231351image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.322268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.412792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.513606image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.616934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.714185image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.809230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.899693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:12.995978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.091706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.188220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.287863image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.390598image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.487653image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.583991image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.779333image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.874575image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:13.970974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.066878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.167238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.269426image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.367652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.462919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.555219image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.653298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:14.750632image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-05-12T14:51:17.172270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-05-12T14:51:17.315608image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-05-12T14:51:17.459819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-05-12T14:51:17.608538image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-05-12T14:51:14.921378image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:15.089201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:15.224108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-05-12T14:51:15.314019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Sample

First rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
0614872.035.00.033.60.62750.01
118566.029.00.026.60.35131.00
2818364.00.00.023.30.67232.01
318966.023.094.028.10.16721.00
4013740.035.0168.043.12.28833.01
5511674.00.00.025.60.20130.00
637850.032.088.031.00.24826.01
7101150.00.00.035.30.13429.00
8219770.045.0543.030.50.15853.01
9812596.00.00.00.00.23254.01

Last rows

PregnanciesGlucoseBloodPressureSkinThicknessInsulinBMIDiabetesPedigreeFunctionAgeOutcome
758110676.00.00.037.50.19726.00
759619092.00.00.035.50.27866.01
76028858.026.016.028.40.76622.00
761917074.031.00.044.00.40343.01
76298962.00.00.022.50.14233.00
7631010176.048.0180.032.90.17163.00
764212270.027.00.036.80.34027.00
765512172.023.0112.026.20.24530.00
766112660.00.00.030.10.34947.01
76719370.031.00.030.40.31523.00